A multiple-input-multiple-output (MIMO) communication system employs multiple transmit antennas in a transmitter and multiple receive antennas in a receiver for data transmission. A MIMO channel formed by the transmit and receive antennas may be decomposed into independent channels, wherein each channel is a spatial sub-channel (or a transmission channel) of the MIMO channel and corresponds to a dimension. The MIMO system can provide improved performance (e.g., increased transmission capacity) if the additional dimensionalities created by the multiple transmit and receive antennas are utilized.
MIMO increases system link robustness and spectral efficiency. To optimize spectral efficiency for MIMO system, many efforts have been made, which can be broadly classified into two categorists: open-loop approaches and closed-loop approaches. The open-loop approaches include spatial multiplexing, space-time coding and the tradeoff therebetween them. The closed-loop approaches focus on maximizing the link capacity, which results in a “water-filling” solution, and on minimizing the weighted MMSE which provides an “inverse water-filling” solution.
In an open-loop MIMO system, the MIMO transmitter has no prior knowledge of the channel condition (i.e., channel state information). As such, space-time coding techniques are usually implemented in the transmitter to combat fading channels. In a closed-loop system, the channel state information (CSI) can be fed back to the transmitter from the receiver, wherein some pre-processing can be performed at the transmitter in order to separate the transmitted data streams at the receiver side.
Such techniques are referred to as beamforming techniques, which provide better performance in desired receiver's directions and suppress the transmit power in other directions. Beamforming techniques are considered for IEEE 802.11n (high throughput WLAN) standard. Closed-loop eigen-beamforming generally provides higher system capacity compared with the closed loop solution, assuming the transmitter knows the down-link channel. Singular value decomposition (SVD) based eigen-beamforming decomposes the correlated MIMO channel into multiple parallel pipes.
When applying the closed loop approach to MIMO-OFDM, the optimal solution requires a bit loading and power loading per OFDM subcarrier, i.e., adapting the transmitted signal x and the power loading matrix P simultaneously per subcarrier. In order to simplify the complexity, conventional approaches propose: (1) adapting coding/modulation and power level across all subcarriers (described in S. A. Mujtaba, “TGn Sync Proposal Technical Specification”, a contribution to IEEE 802.11, 11-04-889rl, November 2004 (incorporated herein by reference)), (2) fixing coding/modulation for all data streams and only adjusting the power level, and (3) fixing coding/modulation and the power loading level (unequal) for all OFDM symbols. The first approach provides the highest throughput with the most implementation complexity. In the second approach above, different levels of power quantization with auto-detection have been evaluated, with 2 level quantization recommended. Such methods are robust for different antenna configurations, channel condition etc., but result in higher cost due to receiver complexity (auto-detection is required). In order to further simplify the receiver complexity, the third approach above proposes 1-bit (const) power loading has been utilized. Since constant power loading is used, no auto-detection is needed. However, such an approach only works well when transmitter and receiver antennas are symmetric. In case of asymmetric transmission, constant power loading leads to performance degradation.
An uneven power loading detection approach, wherein auto-detection at the receiver is not necessary with adaptive power loadings, has also been proposed. However, the receiver must implement an Upper-triangular Decomposition (UD) in addition to the MMSE MIMO detection. Therefore, this approach cannot be used for dummy receivers without beamforming capability. A dummy receiver is the receiver with minimum complexity and only support basics MIMO detection. For example, only general MMSE linear detection, not SVD or UD kind of particular matrix operation.